code: def _get_random_args(args, interface, num, seed, bounds):
      r"""Generate random arguments of a given structure.
  
      Args:
          args (tuple): Original input arguments
          interface (str): Interface of the QNode into which the arguments will be fed
          num (int): Number of random argument sets to generate
          seed (int): Seed for random generation
          bounds (tuple[int]): Range within which to sample the random parameters.
  
      Returns:
          list[tuple]: List of length ``num`` with each entry being a random instance
          of arguments like ``args``.
  
      This function generates ``num`` many tuples of random arguments in the given range
      that have the same shapes as ``args``.
      """
      width = bounds[1] - bounds[0]
      if interface == "tf":
          import tensorflow as tf  # pylint: disable=import-outside-toplevel
  
          tf.random.set_seed(seed)
          rnd_args = []
          for _ in range(num):
              _args = (
                  tf.random.uniform(tf.shape(_arg)) * width + bounds[0]
                  for _arg in args
              )
              _args = tuple(
                  tf.Variable(_arg)
                  if isinstance(arg, tf.Variable)
                  else _arg
                  for _arg, arg in zip(_args, args)
              )
              rnd_args.append(_args)
      elif interface == "torch":
          import torch  # pylint: disable=import-outside-toplevel
  
          torch.random.manual_seed(seed)
          rnd_args = [
              tuple(
                  torch.rand(np.shape(arg)) * width + bounds[0]
                  for arg in args
              )
              for _ in range(num)
          ]
      else:
          rng = np.random.default_rng(seed)
          rnd_args = [
              tuple(
                  rng.random(np.shape(arg)) * width + bounds[0]
                  for arg in args
              )
              for _ in range(num)
          ]
          if interface == "autograd":
              # Mark the arguments as trainable with Autograd
              rnd_args = [
                  tuple(pnp.array(a, requires_grad=True) for a in arg)
                  for arg in rnd_args
              ]
  
      return rnd_args
  
file_path: pennylane/pennylane/math/is_independent.py
imports: import warnings
  import numpy
  from autograd.tracer import isbox, new_box, trace_stack
  from autograd.core import VJPNode
  from pennylane import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: _get_random_args(args, interface, num, seed, bounds) and returns: rnd_args